Drivers of car services such as Taxis, Black cars, Limos and transportation networks (such as Uber™, Lyft™ and Sidecar™ drivers) spend almost half their time driving a vacant car (see e.g. NYC 2014 taxicab factbook). These miles are referred to as “dead miles” and cost the driver (lost income, waste of fuel), the passengers (wasted wait time) and society (unnecessary road congestion and pollution). During this time, drivers typically face a slew of options and must make many different decisions on a typical day. For example, each time they drop-off a passenger, they have many options on what to do next. They mostly make these decisions based on their intuition and past experience. Thus, improving this decision making process will result in increased utilization/profitability of drivers and car service fleets as well as mitigation of the problems described above.
Example: a NYC yellow cab driver can select several different routes to cruise searching for the next passenger, or can alternatively drive to a nearby taxi station. Another type of driver, such as one who drives in a transportation network (such as an UberX™ driver driving with Uber's™ mobile application), which works in a system which matches supply and demand based on proximity of drivers to passengers, can select a standpoint in which he believes he has a high chance of getting the next passenger as soon as possible. He may sometimes prefer a distant point with a high probability of getting a passenger over a closer point which he believes is less probable of getting a passenger soon. Such a driver has plenty of inputs available which provide information he can rely on in his decisions, and which are used in order to decide on all of these questions, eventually, however, the decision he makes is largely based on intuition and not on a true analysis of the situation.